silobuster 1.0.3

A package to manage data. This package provides means to connect to different data sources and transform the data using Pandas. The focus of the library is to canonicalize data sources. This project is focused on Human Resources data and working with data in various formats. Dedupe.io's machine learning library is used to cluster various data. Please visit our documentation at https://openreferral.github.io/silobuster-model-trainer/ to learn more.

pip install silobuster

The author of this package has not provided a project description

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

silobuster-1.0.3.tar.gz (901.9 kB view details)

Uploaded May 7, 2023 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

silobuster-1.0.3-py3-none-any.whl (971.1 kB view details)

Uploaded May 7, 2023 Python 3

File details

Details for the file silobuster-1.0.3.tar.gz.

File metadata

Download URL: silobuster-1.0.3.tar.gz
Upload date: May 7, 2023
Size: 901.9 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for silobuster-1.0.3.tar.gz
Algorithm	Hash digest
SHA256	`803ccb10eb538de70899e29d8a2afc831b556aece1d6917dbaaea69413e04b1e`
MD5	`0258a78972843ac4f406044f1ee68177`
BLAKE2b-256	`939a5702d6640dddf56ab583715872081025e41c56e1bc17c66574f366416a1b`

See more details on using hashes here.

File details

Details for the file silobuster-1.0.3-py3-none-any.whl.

File metadata

Download URL: silobuster-1.0.3-py3-none-any.whl
Upload date: May 7, 2023
Size: 971.1 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.1 CPython/3.11.3

File hashes

Hashes for silobuster-1.0.3-py3-none-any.whl
Algorithm	Hash digest
SHA256	`13bfd3c8905e4045148ca7d9182170b273d5d5ab3a19431c3567809ed3a2d46e`
MD5	`86cda58b50c7790def8fdf30dad14b53`
BLAKE2b-256	`b941b933058e1a2f983993cd369a318ee9b4e3539cd7113c90958f3911786ce3`